Search

This paper presents an advanced nonlinear control design for the servo layer in typical EP printers, which complements our previous work focusing on control of supervisory layer. A control centric modeling and reduction approach for the main motor in the servo layer (i.e., a PWM three-phase permanent magnet brushless dc motor) is conducted. The set of differential equations and the corresponding state variable model are established for each operation interval. The subsystem model from each interval is combined to form a comprehensive model. Model reduction is performed to reduce the complexity but preserve crucial dynamics of the model. Finally, a robust adaptive fuzzy controller is designed and its feasibility and effectiveness is verified on the comprehensive model.

This paper presents a novel visual servo tracking method for a wheeled mobile robot based on target features and depth of scenery. The robot is equipped with an RGBD sensor to provide both color and depth information of the scene. The proposed method comprises two phases. The first phase utilizes feature matching to ensure that the target moves into the field of vision such that the robot may enter the second phase. The second phase implements visual servo aiming at object tacking accuracy. The depth information is employed in the proposed approach, which reduces the computational complexity required as in conventional approaches based on feature extraction. In addition, the second phase has built-in mechanism for detecting and exiting local minimum when the robot speed converges. The method is also capable of sequentially tracking multiple objects. Experimental results demonstrate the feasibility and effectiveness of the proposed two-phase method.

This paper proposes a new method for vehicle license plate recognition on the basis of Extreme Learning Machine. ELM is a new category of neural networks which possesses compelling characteristics essential for license plate recognition, such as low computational complexity, fast training, and good generalization (as opposed to traditional neural networks). The proposed method studies incorporation of three ELMs (i.e., basic ELM, incremental ELM, and enhanced incremental ELM) into a typical pipeline for automatic license plate recognition. In the preliminary study (under Windows PC with MATLAB), the success rate of recognition is 87.5% with execution time of 0.3s. A comparative study also shows that the proposed method outperforms conventional approaches (template matching, edge statistics, RBF, and SVM) in terms of accuracy and speed.

In most scenes, images captured by digital devices are greatly influenced by environmental illuminant. Because there are different illuminants in the scene, the color from objects even change and be similar to the illuminant color. It results that the objects cannot reflect the true color and this phenomenon is called color cast. In this thesis, by utilizing basic color science and experimental results, a dataset of color sample under different illuminants is constructed. Using the existing method of illuminant estimation to calculate plural color temperatures of the input image, called the initial estimated color temperature. Furthermore, utilizing clustering algorithm to execute illuminant classification to initial plural color temperatures, and calculating the multi-determined color temperatures of image. This study takes the lead in utilizing the spatial fuzzy c-means (SFCM) clustering algorithm to execute the illuminant classification. In traditional white balance, the correction configuration focuses on single illuminant scene, which is fixed correction value, whereas there are multi-illuminant scenes mostly in real scene, the performance of white balance is not good. In this thesis, the adopted SFCM algorithm revises the main illuminant individually and solves the color cast successfully.

This paper presents a control centric modeling and analysis approach along with a nonlinear control design for a class of switching power converters. A comprehensive model combining the respective state variable models of the interval subsystems is established. Comparison with PSpice simulation justifies the credibility of the model. Based on this model, internal/BIBO stability can be studied for each interval subsystem. Moreover, controllability and observability can also be analyzed to help determining subsequent control configuration. The established model is further investigated for advanced control design, i.e., robust adaptive fuzzy control.

This paper presents FPGA hardware implementation of a type-2 TSK fuzzy neural system for illuminant estimation, i.e., color temperature, based only on the contents of the image along with performing white balance for the image. The implementation is conducted on an FPGA development platform with Xilinx artix-7 XC7A200 chip. Experiments and comparative study are performed. It is shown that the proposed FPGA design can operate in real-time. For generic 1080p image, it can reach the frame rate of 51.598 frames per second at maximal operation frequency for illuminant estimation and white balance. This resolves the slow execution speed of the proposed algorithm on software platform. The results demonstrate major advancements of the proposed approach over previous and existing methods.

This paper presents a type-2 TSK fuzzy neural approach to establish a model for estimation of unknown illuminant, i.e., color temperature, of a digitally captured image based only on the contents of the image. The estimated temperature may be further utilized to perform white balance for the image. The results demonstrate major advancements of the proposed approach over previous and existing methods. The comparative study also suggests that the proposed method is more robust to generic images and varieties of illuminants and scenes than do existing approaches.

This paper presents a fuzzy neural approach to establish a model for estimation of unknown illuminant, i.e., color temperature, of a digitally captured image based only on the contents of the image. The estimated temperature may be further utilized to perform white balance for the image. The results demonstrate strong improvement of the proposed approach over a previous work. The comparative study also suggests that the proposed method is more adaptable to generic images and varieties of illuminants and scenes than other existing methods do. Hardware implementation of the algorithm based on FPGA is also presented in this work.

Color balance has become an important part of image processing pipeline. It forms a class of image processing algorithms for removal of color cast in digitally captured images. This work presents a fuzzy neural approach to establish a model for estimation of adjusting ratios for the color separates of a digitally captured image based only on the contents of the image. The estimated ratios are then utilized to perform color balance for the image. The comparative study suggests that the proposed method is more adaptable to generic images and varieties of illuminants and scenes than other existing methods do. Hardware implementation of the algorithm based on FPGA is also presented in this work.

This paper proposes a novel color balance technique by establishing a fuzzy inference system. A database is constructed to include certain properties of various color samples captured under standard illuminants. Next, a fuzzy system is established, whose configuration and parameters are determined using the aforementioned database. Finally, test image captured under unknown illuminant may be passed through the fuzzy system to produce a set of ratios for correcting and removing the color cast. An experiment and comparative study justify that the suggested approach is preferable to existing methods with regard to execution time and the performance.

This paper provides a novel color balance algorithm based on fuzzy inference rules by basic concept of color and the experimental results. In this algorithm, the color sample database is forms with the images are captured in different illuminants at first. Second, statistical results of color sample database are used to form fuzzy inference system. Finally, image is adjusted from unknown illuminant to the image in standard illuminant with correction the fuzzy inference system forecast. Experimental results and a comparative study justify that the proposed method is preferable to existing methods with regard to execution time and the performance.

This paper presents an analytical model to address the scenarios of positioning errors for both Hall sensors and phase windings. This model is capable of demonstrating the qualitative features of torque ripples due to the indicated positioning defects. Moreover, it can be employed to investigate and predict various quantitative aspects such as fundamental frequency and corresponding harmonics, effects on average torque, etc. Incorporating this model into a conventional PWM PMBDCM model and conduct a realistic simulation further justify the capability and effectiveness of the proposed analytical model.

This paper presents a high-performance design of dc-dc switching power converter system, which can deliver a regulated 0–50V and 0–15A output. The full-bridge phase-shift converter is realized with zero voltage switching and UCC3895 IC to achieve higher efficiency and pulse width modulation. For wide range of adjustable voltage output control, a PI gain-scheduling control scheme is proposed and implemented using a conventional FPGA chip. Experimental results justify the feasibility and performance of the proposed power converter control system.

In this paper, we propose an image processing pipeline to estimate the color temperature and white balance an image. The method comprises a pre-processing stage to generate training samples for the fuzzy neural network (FNN), which plays the role of predicting pixel-level color temperatures, and two post-processing stages to integrate the outputs of the FNN into an overall color temperature of and white balance the image. Experimental results and comparison study verify that the proposed method performs better than most existing methods.

This paper proposes a systematic approach to conduct system-level optimal parameter design for laser electrophotographic systems. Besides system performance, we incorporate two other practical indices, i.e., cost and energy consumption, into design objectives and formulate a multi-objective optimization problem. Fuzzy logics are employed to provide the nonlinear or linguistic mapping between the decision variables and the design objectives. For comparison purpose, the problem is solved using both single-objective and multi-objective optimization algorithms. A conventional monochrome laser printer serves as the platform for verification of the proposed optimal design approach.

In this paper, we propose a new design of spatial-based repetitive control for rotational motion systems required to operate at varying speeds and subject to spatially periodic disturbances. The system has known model structure with uncertain parameters. To synthesize a repetitive controller in spatial domain, a linear time-invariant system is reformulated with respect to a spatial coordinate (e.g., angular displacement), which results in a nonlinear system. A nonlinear state observer is then established for the system. Adaptive backstepping is applied to the system with the state observer so as to stabilize the system and reduce the tacking error. Moreover, a spatial-based repetitive controller is added and operates in parallel with the adaptively backstepped system, which further reduces the tracking error. The overall output feedback adaptive backstepping repetitive control system is robust to structured parameter uncertainty, capable of rejecting spatially periodic disturbances under varying process speeds, and can be shown to be stable and produce bounded state estimated error and bounded tracking error under sensible assumptions. Finally, feasibility and effectiveness of the proposed scheme is verified by simulation.

This paper extends the author's previous work on spatial-based repetitive control. We propose another repetitive control design for rotational motion systems required to operate at various speeds, and subject to structured parameter uncertainty and spatially periodic disturbances. To synthesize a repetitive controller in spatial domain, a linear time-invariant system is reformulated with respect to a spatial coordinate (e.g., angular displacement), which results in a nonlinear system. Adaptive feedback linearization is applied to linearize the system while seeking the correct system parameters online. Then, a spatial-based reduced-order repetitive controller along with a stabilizing controller is designed and operates in parallel with the adaptively feedback linearized system. The overall adaptive repetitive control system is thus robust to structured parameter uncertainty and spatially periodic disturbances under variable process speed. Feasibility and effectiveness of the proposed scheme is verified by simulation.

In this paper, we propose a new design of spatial-based repetitive control system for rotational motion systems required to operate at various speeds, and subject to structured parameter uncertainty and spatially periodic disturbances. To synthesize a repetitive controller in spatial domain, a linear time-invariant system is reformulated with respect to a spatial coordinate (e.g., angular displacement), which results in a nonlinear system. Adaptive backstepping is applied to stabilize the system while adapting the parameters at the same time. Then, a spatial-based reduced-order repetitive controller along with a stabilizing controller is designed and operates in parallel with the adaptive backstepping system. The overall spatial-based adaptive backstepping repetitive control system is thus robust to structured parameter uncertainty. Also its capability of rejecting spatially periodic disturbances is invariant to process speed. Feasibility and effectiveness of the proposed scheme is verified by simulation.

Repetitive controllers have been shown to work well for tracking periodic reference commands or for rejecting periodic disturbances in regulation applications. Typical repetitive controllers are synthesized in a temporal domain. For motor/gear transmission systems, disturbances due to gear eccentricity or tooth profile error are spatially periodic. This implies that the temporal frequency values for these two types of disturbances are proportional to the nominal angular velocity and vary accordingly. A repetitive controller designed based on the temporal information of disturbances cannot perform well as the spatially periodic disturbances also affect the nominal angular velocity. We reformulate the temporal domain system with respect to a spatial coordinate, which resulted in a nonlinear position invariant (NPI) system. By treating the angular velocity as varying but measurable parameters, the nonlinear NPI system can be treated as a linear parameter varying (LPV) system, for which an LPV gain-scheduling repetitive controller (LPVRC) with anti-windup can be designed. Experimental results on the velocity regulation of the photosensitive drum in a laser printer verified the effectiveness of the proposed LPVRC design.

Halftone banding, which affects the uniformity of xerographic images, is a well-known artifact for xerography, which appears as periodic light and dark bands across a printed page perpendicular to the process or print direction. Effective approaches for banding compensation need to consider the following two unique characteristics: 1) The human visual system should be included into the control system synthesis to quantify the perceived banding; 2) Spatially periodic scanline spacing variation due to gear noise has been shown to be a major contributor. In this paper, we summarize the work to incorporated human visual model into control system design and point out one aspect of the human visual system model that need to be addressed. To handle position dependent periodic disturbances, we employ spatial sampling and reformulate the motion transmission system using angular displacement as the independent variable. A linear parameter varying repetitive controller (LPVRC) is proposed to compensate for the angular displacement dependent periodic disturbances caused by the gears. Experimental verifications of the proposed approach are presented.

This paper proposes a new process control strategy for reducing banding artifacts in electrophotographic (EP) processes. EP banding artifact is shown to correlate to the fluctuation of the organic photoconductive (OPC) drum angular velocity. Improved regulation of the OPC drum rotational velocity under various process uncertainty and variations will significantly improve EP process stability and reduce the appearance of banding. The proposed control strategy includes two levels of OPC drum speed regulation. The first level, utilizes a loop shaping technique to incorporate a human visual system (HVS) model into the control loop to eliminate low frequency and non-periodic drum velocity fluctuation. The second level uses an internal model based repetitive controller to reduce the effect of periodic velocity fluctuations. The HVS based loop shaping design is intended to address the subjective evaluation of a printing process by incorporating human visual perception into EP process control. The experimental verification on a typical low cost 600-dpi EP engine showed significant banding reduction for spatial frequency up to 70 cycles per inch.